[565] | 1 | #!/usr/bin/env python3 |
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| 2 | # -*- coding: utf-8 -*- |
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[596] | 3 | |
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[565] | 4 | import sys |
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| 5 | import numpy as np |
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| 6 | from sklearn import manifold |
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[596] | 7 | |
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| 8 | #to make it work in console, http://stackoverflow.com/questions/2801882/generating-a-png-with-matplotlib-when-display-is-undefined |
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| 9 | #import matplotlib |
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| 10 | #matplotlib.use('Agg') |
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| 11 | |
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[565] | 12 | import matplotlib.pyplot as plt |
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| 13 | from mpl_toolkits.mplot3d import Axes3D |
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| 14 | from matplotlib import cm |
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| 15 | import argparse |
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| 16 | |
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[596] | 17 | |
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[598] | 18 | #http://www.nervouscomputer.com/hfs/cmdscale-in-python/ |
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| 19 | def cmdscale(D): |
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| 20 | """ |
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| 21 | Classical multidimensional scaling (MDS) |
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| 22 | |
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| 23 | Parameters |
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| 24 | ---------- |
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| 25 | D : (n, n) array |
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| 26 | Symmetric distance matrix. |
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| 27 | |
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| 28 | Returns |
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| 29 | ------- |
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| 30 | Y : (n, p) array |
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| 31 | Configuration matrix. Each column represents a dimension. Only the |
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| 32 | p dimensions corresponding to positive eigenvalues of B are returned. |
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| 33 | Note that each dimension is only determined up to an overall sign, |
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| 34 | corresponding to a reflection. |
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| 35 | |
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| 36 | e : (n,) array |
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| 37 | Eigenvalues of B. |
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| 38 | |
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| 39 | """ |
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| 40 | # Number of points |
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| 41 | n = len(D) |
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| 42 | |
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| 43 | # Centering matrix |
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| 44 | H = np.eye(n) - np.ones((n, n))/n |
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| 45 | |
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| 46 | # YY^T |
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| 47 | B = -H.dot(D**2).dot(H)/2 |
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| 48 | |
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| 49 | # Diagonalize |
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| 50 | evals, evecs = np.linalg.eigh(B) |
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| 51 | |
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| 52 | # Sort by eigenvalue in descending order |
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| 53 | idx = np.argsort(evals)[::-1] |
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| 54 | evals = evals[idx] |
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| 55 | evecs = evecs[:,idx] |
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| 56 | |
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| 57 | # Compute the coordinates using positive-eigenvalued components only |
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| 58 | w, = np.where(evals > 0) |
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| 59 | L = np.diag(np.sqrt(evals[w])) |
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| 60 | V = evecs[:,w] |
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| 61 | Y = V.dot(L) |
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| 62 | |
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| 63 | return Y, evals |
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[596] | 64 | |
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[565] | 65 | def rand_jitter(arr): |
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| 66 | stdev = arr.max() / 100. |
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| 67 | return arr + np.random.randn(len(arr)) * stdev * 2 |
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| 68 | |
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| 69 | |
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| 70 | def read_file(fname, separator): |
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| 71 | distances = np.genfromtxt(fname, delimiter=separator) |
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| 72 | if np.isnan(distances[0][len(distances[0])-1]):#separator after the last element in row |
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| 73 | distances = np.array([row[:-1] for row in distances]) |
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| 74 | return distances |
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| 75 | |
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| 76 | |
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| 77 | def compute_mds(distance_matrix, dim): |
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[598] | 78 | embed, evals = cmdscale(distance_matrix) |
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[600] | 79 | |
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[599] | 80 | variances = [np.var(embed[:,i]) for i in range(len(embed[0]))] |
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[600] | 81 | percent_variances = [sum(variances[:i+1])/sum(variances) for i in range(len(variances))] |
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[598] | 82 | for i,pv in enumerate(percent_variances): |
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| 83 | print(i+1,"dimension:",pv) |
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| 84 | |
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[600] | 85 | dim = min(dim, len(embed[0])) |
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| 86 | embed = np.asarray([embed[:,i] for i in range(dim)]).T |
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| 87 | |
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[565] | 88 | return embed |
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| 89 | |
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| 90 | |
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| 91 | def plot(coordinates, dimensions, jitter=0, outname=""): |
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| 92 | fig = plt.figure() |
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| 93 | |
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| 94 | if dimensions < 3: |
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| 95 | ax = fig.add_subplot(111) |
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| 96 | else: |
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| 97 | ax = fig.add_subplot(111, projection='3d') |
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| 98 | |
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| 99 | add_jitter = lambda tab : rand_jitter(tab) if jitter==1 else tab |
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| 100 | |
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| 101 | x_dim = len(coordinates[0]) |
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| 102 | y_dim = len(coordinates) |
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| 103 | |
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| 104 | ax.scatter(*[add_jitter(coordinates[:, i]) for i in range(x_dim)], alpha=0.5) |
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| 105 | |
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| 106 | plt.title('Phenotypes distances') |
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| 107 | plt.tight_layout() |
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| 108 | plt.axis('tight') |
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| 109 | |
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| 110 | if outname == "": |
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| 111 | plt.show() |
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| 112 | |
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| 113 | else: |
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| 114 | plt.savefig(outname+".pdf") |
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[598] | 115 | np.savetxt(outname+".csv", coordinates, delimiter=";") |
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[565] | 116 | |
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| 117 | |
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[597] | 118 | def main(filename, dimensions=3, outname="", jitter=0, separator='\t'): |
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[598] | 119 | dimensions = int(dimensions) |
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[565] | 120 | distances = read_file(filename, separator) |
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| 121 | embed = compute_mds(distances, dimensions) |
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| 122 | |
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| 123 | if dimensions == 1: |
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| 124 | embed = np.array([np.insert(e, 0, 0, axis=0) for e in embed]) |
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| 125 | |
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[597] | 126 | plot(embed, dimensions, jitter, outname) |
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[565] | 127 | |
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| 128 | |
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| 129 | if __name__ == '__main__': |
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| 130 | parser = argparse.ArgumentParser() |
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| 131 | parser.add_argument('--in', dest='input', required=True, help='input file with dissimilarity matrix') |
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| 132 | parser.add_argument('--out', dest='output', required=False, help='output file name without extension') |
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| 133 | parser.add_argument('--dim', required=False, help='number of dimensions of the new space') |
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| 134 | parser.add_argument('--sep', required=False, help='separator of the source file') |
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| 135 | parser.add_argument('--j', required=False, help='for j=1 random jitter is added to the plot') |
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| 136 | |
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| 137 | args = parser.parse_args() |
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| 138 | set_value = lambda value, default : default if value == None else value |
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| 139 | main(args.input, set_value(args.dim, 3), set_value(args.output, ""), set_value(args.j, 0), set_value(args.sep, "\t")) |
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